Query Mesh- A Novel Paradigm for Query Processing
Technological advances in positioning, sensor and monitoring technology drive data acquisition devices to generate massive streams of data. The goal of this research is to develop a new class of high-performance stream data management systems capable of coping with scenarios with infinite data arriving in large volumes, and with near-real time response requirements. The proposed query processing paradigm, termed the multi-route query mesh model (QM), overcomes a major limitation in current query optimizers, both static and stream ones alike, namely the assignment of a single `best' query execution plan for all input data. This approach, being based on the strong assumption of data uniformity, results in substandard performance for possibly all data items. Instead, query mesh adopts a processing structure composed of a data classifier and a multiple route plan infrastructure. Different learning models can be plugged as classifier logic into the QM model. Given the complexity of the QM solution space, cost-based search heuristics are designed to efficiently find high-quality query meshes. QM is adaptive supporting the detection and incremental modification of the QM classifier and its routes. Intellectual merit lies in the design, development and evaluation of a novel multi-route paradigm for stream query processing, -- a perfect middle ground between the two current extremes of single-plan versus route-less solutions. Experimental studies compare query mesh to state-of-the-art solutions. QM impacts society by facilitating a wide range of stream-centric applications, including medical out-patient monitoring, emergency management, and business intelligence processing, and by integrating project activities with education.
Acknowledgments:This work is supported by
NSF
Projects IIS-0917017 and 0551584 (equipment grant).
